Redundant vehicle control systems based on tire sensors - load estimation

ABSTRACT

A control system for controlling one or more torque generating devices on a heavy-duty vehicle comprising a primary sensor system with a primary sensor control unit configured to interpret an output signal of the primary sensor system, wherein the primary sensor control unit is configured to determine a first load value associated with the heavy-duty vehicle, and one or more tire sensor devices mounted on one or more tires of the heavy-duty vehicle, and a tire sensor control unit configured to interpret an output signal of the one or more tire sensor devices, wherein the tire sensor control unit is configured to determine a second load value associated with the heavy-duty vehicle, wherein the control system is arranged to base control of the heavy-duty vehicle on the second load value in case of malfunction in the primary sensor system and/or in the primary sensor control unit.

RELATED APPLICATIONS

The present application claims priority to European Patent ApplicationNo. 21212078.6, filed on Dec. 2, 2021, and entitled “REDUNDANT VEHICLECONTROL SYSTEMS BASED ON TIRE SENSORS—LOAD ESTIMATION,” which isincorporated herein by reference in its entirety.

TECHNICAL FIELD

The present disclosure relates to methods and systems for controllingmotion of heavy-duty vehicles, and in particular to redundant systemsable to provide at least a rudimentary control function despitemalfunction of one or more vehicle components. The invention can beapplied in heavy-duty vehicles, such as trucks and constructionequipment, but is not restricted to this particular type of vehicle.

BACKGROUND

Most heavy-duty vehicles comprise one or more vehicle control units(VECU) arranged to assist the driver in maneuvering the vehicle, e.g.,as part of an advanced driver assistance system (ADAS). Autonomous orsemi-autonomous vehicles of course rely to a great extent on VECUs forvehicle motion control.

For instance, VECU controlled anti-lock braking systems (ABS) step in toassume control in case one or more of the wheels of the vehicle lockduring braking. Similarly, a traction control system (TCS), also knownas ASR (from German: Antriebsschlupfregelung), assumes control in casethe wheels of the vehicle spin out of control during acceleration. TCSis typically part of the overall electronic stability control (ESC) on aheavy-duty vehicle, which relies on one or more VECUs to provide morestable vehicle operation.

Strict safety requirements are often imposed on heavy-duty vehicles, andin particular on autonomous vehicles, such as level four (L4) and levelfive (L5) autonomous vehicles. Heavy-duty vehicles are thereforenormally required to implement both redundant control and actuationsystems, meaning that at least one back-up system should be possible toactivate in order to provide at least a rudimentary control function incase one or more primary vehicle control systems suffer malfunction orsome form of outage.

One option for implementing redundancy in a heavy-duty vehicle is tosimply deploy two or more of every important component and system in thevehicle, however, this approach can be very costly and will take upvaluable space which could otherwise be used for value-adding featuressuch as extra battery capacity in an electric vehicle. Designing, forexample, two separate and independent braking systems by simplymultiplying the components is inefficient in terms of packaging,function performance, and system performance.

There is a need for more efficient ways of providing redundancy in aheavy-duty vehicle. In particular, there is a need for redundant vehiclesensor systems and control units configured to interpret the outputsignals from the redundant vehicle sensors in a robust manner forredundancy purposes.

SUMMARY

It is an object of the present disclosure to provide redundant motioncontrol systems for heavy-duty vehicles. This object is at least in partobtained by a control system for controlling one or more torquegenerating devices on a heavy-duty vehicle. The system comprises aprimary sensor system with a primary sensor control unit configured tointerpret an output signal of the primary sensor system, wherein theprimary sensor control unit is configured to determine a first loadvalue associated with the heavy-duty vehicle, i.e., a normal forceacting on one or more wheels of the heavy-duty vehicle. The system alsocomprises one or more tire sensor devices mounted on one or more tiresof the heavy-duty vehicle, and a tire sensor control unit configured tointerpret an output signal of the one or more tire sensor devices,wherein the tire sensor control unit is configured to determine a secondload value associated with the heavy-duty vehicle. The control system isarranged to base control of the heavy-duty vehicle on the second loadvalue in case of malfunction in the primary sensor system and/or in theprimary sensor control unit, and on the first load value otherwise.Again, a redundancy system is provided based on tire sensor technologywhich does not take up valuable space on the axles or the chassis of theheavy-duty vehicle. The tire sensor systems can be designed to beindependent from the other vehicle sensor systems, which is an advantageif the tire sensors are to be used as a redundant system.

According to aspects, the primary sensor system comprises a sensorconfigured in connection to a suspension system of the heavy-dutyvehicle. The suspension system is normally used to estimate vehicleload. The tire sensors provide a redundant sensor system to complementsuch suspension-based load estimation systems. The one or more tiresensor devices comprises any of; an accelerometer, a strain gauge, andan optical sensor, wherein the output data of the tire sensor controlunit comprises the second load value. These sensor types can be designedseparately from the suspension, thereby providing an independent sensorsystem advantageously used for redundancy purposes.

According to aspects, the primary sensor control unit is configured tocontrol a primary valve system for brake control of the heavy-dutyvehicle, wherein the tire sensor control unit is configured to control asecondary valve system for brake control of the heavy-duty vehicleseparate from the primary valve system. This way a redundant brakecontrol system can be provided based on smart tire technology. Thesystem is reliable and relatively low-cost. The tire sensors areseparate and independent from the other vehicle sensor systems, which isan advantage.

According to aspects, the tire sensor control unit is configured tointerpret the output signals of the one or more tire sensor devicesbased on a machine learning (ML) algorithm. Machine learning has beenshown to provide efficient and stable signal processing functionssuitable for use with tire sensor systems. Also, the ML algorithms canbe trained to function with different types of tire sensors, and alsousing output data from two or more tire sensor types, which is anadvantage since a more robust system is obtained. A physics guided MLalgorithm can of course also be used.

According to aspects, the output data of the tire sensor control unitcomprises a wheel slip ratio associated with a wheel on the heavy-dutyvehicle. Thus, a redundant wheel-slip based control system is provided,which is an advantage.

There is also disclosed herein control units, computer programs,computer readable media, computer program products, brake systems,propulsion systems, methods and vehicles associated with the abovediscussed advantages.

Generally, all terms used in the claims are to be interpreted accordingto their ordinary meaning in the technical field, unless explicitlydefined otherwise herein. All references to “a/an/the element,apparatus, component, means, step, etc.” are to be interpreted openly asreferring to at least one instance of the element, apparatus, component,means, step, etc., unless explicitly stated otherwise. The steps of anymethod disclosed herein do not have to be performed in the exact orderdisclosed, unless explicitly stated. Further features of, and advantageswith, the present invention will become apparent when studying theappended claims and the following description. The skilled personrealizes that different features of the present invention may becombined to create embodiments other than those described in thefollowing, without departing from the scope of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

With reference to the appended drawings, below follows a more detaileddescription of embodiments of the invention cited as examples. In thedrawings:

FIGS. 1A-B schematically illustrate some example heavy-duty vehicles;

FIG. 2 shows an example redundant vehicle brake/propulsion system;

FIG. 3 shows an example vehicle control system comprising a tire sensor;

FIG. 4 illustrates a dependency between tire forces and wheel slipratio;

FIG. 5 shows an example vehicle control architecture;

FIGS. 6A-B are flow charts illustrating methods;

FIG. 7 schematically illustrates an example control unit;

FIG. 8 shows an example computer program product; and

FIGS. 9-12 are graphs showing example tire sensor output signals.

DETAILED DESCRIPTION

The invention will now be described more fully hereinafter withreference to the accompanying drawings, in which certain aspects of theinvention are shown. This invention may, however, be embodied in manydifferent forms and should not be construed as limited to theembodiments and aspects set forth herein; rather, these embodiments areprovided by way of example so that this disclosure will be thorough andcomplete, and will fully convey the scope of the invention to thoseskilled in the art. Like numbers refer to like elements throughout thedescription.

It is to be understood that the present invention is not limited to theembodiments described herein and illustrated in the drawings; rather,the skilled person will recognize that many changes and modificationsmay be made within the scope of the appended claims.

FIGS. 1A and 1B illustrate example vehicles 100 for cargo transportwhere the herein disclosed techniques can be applied with advantage.FIG. 1A shows a truck supported on wheels 120, 140, and 160, at leastsome of which are driven wheels and at least some of which are brakedwheels.

FIG. 1B shows a semitrailer vehicle where a tractor unit 101 tows atrailer unit 102. The front part of the trailer unit 102 is supported bya fifth wheel connection 103, while the rear part of the trailer unit102 is supported on a set of trailer wheels 180.

With reference also to FIG. 2 , each wheel, or at least a majority ofthe wheels on the vehicle, is associated with a respective wheel servicebrake 130, 150, 170 (trailer unit wheel brakes are not indicated inFIGS. 1A-1B). This wheel service brake may, e.g., be a pneumaticallyactuated disc brake or a drum brake, or an electromechanical brake. Thewheel brakes are controlled by one or more primary brake control units(BCU) 220 via a brake control valve arrangement in a known manner.Herein, the terms brake controller, brake modulator, and wheel endmodule will be used interchangeably. They are all to be interpreted as adevice which controls applied braking force on at least one wheel of avehicle, such as the vehicle 100. Each of the wheel brake controllers iscommunicatively coupled to a vehicle control unit (VECU) 110, allowingthe control unit to communicate with the brake controllers, and therebycontrol vehicle braking. This control unit may potentially comprise anumber of sub-units distributed across the vehicle, or it can be asingle physical unit. The BCU may also be implemented as a softwaremodule which executes on the VECU hardware. The VECU 110 may, e.g.,perform vehicle motion management functions such as allocating brakeforce between wheels to maintain vehicle stability and keep wheel slipat acceptable levels. The VECU 110 may also perform one or more vehiclestate estimation functions, such as continuously or periodicallyestimating vehicle load, i.e., the normal force F_(z) acting on one ormore wheels of the vehicle 100. The VECU may also control one or morepropulsion devices, i.e., a combustion engine and/or one or moreelectric machines. Torque generating devices such as service brakes andpropulsion devices will be referred to herein as motion support devices(MSD). It is appreciated that a heavy-duty vehicle like thoseillustrated in FIG. 1A and in FIG. 1B may comprise a plurality of MSDsof different type.

Some trailers may also comprise a trailer VECU 115, often operating inslave configuration to the primary VECU 110. The trailer vehicle unit102 may also comprise one or more BCUs 240 to control braking on thewheels 180 of the trailer. The trailer vehicle unit 102 may be a poweredtrailer vehicle unit which comprises propulsion devices in addition tothe brake devices.

At least some of the wheels 120, 140, 160, and 180 comprise respectivetire sensors 210. A tire sensor is a sensor device mounted in directconnection to the tire, such as inside the tire, embedded into the tirethread, or mounted on the wheel rim.

Many different types of tire sensors are known. Most tire sensors arebased on accelerometers, various types of gauges (such as straingauges), and optical devices. There are also tire sensors which comprisesatellite positioning receivers that enable determination of vehiclespeed. Some example tire sensors are described in the below list ofprior art documents;

US2003164036 discloses methods for predicting the forces generated inthe tire contact patch from measurements of tire deformations.

US2019187026 also relate to the determining of wheel forces usingsensors mounted in connection to the tires of a vehicle. The disclosurealso relates to the determination of wheel speed using sensors coupledto the wheel.

WO20081977A discusses tire sensors that may be configured to sense amagnitude of one or more physical quantities such as air pressure of thetire, contact patch area and/or shape, contact forces and adhesioncharacteristics of the road.

US2017113499 relates to estimating a tire state, such as its wear.

WO19127506A1 also relates to determining slip ratio of a tire based ondata obtained from one or more sensors mounted in connection to a tire.

U.S. Pat. No. 6,526,334 relates to a tire sensing system configured todetect individual wheel loads as well as lateral and torsional forcesapplied to individual tires. In addition, the speeds of the individualwheels can be detected.

The present disclosure relates to techniques which exploit known tiresensor technology, such as that in the list above, in order to providevehicle redundancy, in particular when it comes to sensor systems andcontrol units for interpreting sensor output signals. For instance, inFIG. 2 , the BCU 220, 240 may be part of a primary brake control systemwhich comprises a primary sensor system based on, e.g., wheel speedsensors or the like, and a primary sensor control unit (the BCU)configured to interpret the output signals of the primary sensor systemand control braking in dependence of a request from the VECU 110, 115and based on the primary sensor system output signals. A tire sensorcontrol unit (TiCU) is comprised in a secondary (redundant) brakecontrol system which comprises one or more tire sensor devices 210mounted on at least some of the tires 120, 140, 160, 180 of theheavy-duty vehicle 100. The TiCU 230, 250 is configured to interpret theoutput signals of the one or more tire sensor devices. The VECU 110, 115may then base the vehicle control on the primary system as long as thissystem is up and running and deemed to provide reliable output, and fallback to the secondary system in case some form of malfunction isdetected in the primary system. There are many known ways in whichmalfunction can be detected, e.g., based on a challenge response system,where malfunction is detected in case there is no response from thesystem in a pre-determined amount of time. Also, power outage may beused to infer that a malfunction has occurred. This way the vehicle 100can be equipped with redundant control systems based on smart tires,i.e., tires comprising tire sensors and a tire sensor control unit, orTiCU 230, 250.

Unreliable data can also be seen as a form of malfunction. For instance,in case the vehicle experiences outage in a satellite-based positioningsystem.

It is appreciated that a redundancy system can be implemented in atrailer vehicle unit regardless of whether the tractor 101 comprisestire sensors, as also shown in FIG. 2 , where a primary brake controlsystem comprises a BCU 240 configured to operate based on data from aprimary sensor system, and a TiCU forming part of the backup system,i.e., the secondary system which then bases its control on one or moretire sensors 210 assembled in connection to the tires and/or the wheelsof the trailer vehicle unit 102.

FIG. 3 schematically illustrates functionality 300 for controlling awheel 310 by some example motion support devices (MSDs) here comprisinga friction brake 320 (such as a disc brake or a drum brake) and apropulsion device 340 (such as an electric machine or a combustionengine).

The friction brake 320 and the propulsion device 340 are examples ofwheel torque generating devices, which may also be referred to asactuators and which can be controlled by one or more MSD control units330. The control is based on, e.g., measurement data obtained from awheel speed sensor and/or from other vehicle state sensors 380, such asradar sensors, lidar sensors, and also vision based sensors such ascamera sensors and infra-red detectors. Other example torque generatingmotion support devices which may be controlled according to theprinciples discussed herein comprise engine retarders and power steeringdevices. An MSD control unit 330 may be arranged to control one or moreactuators. For instance, it is not uncommon that an MSD control unit 330is arranged to control both wheels on an axle.

A traffic situation management (TSM) function or a driver 370 plansdriving operation with a time horizon of, e.g., 10 seconds or so. Thistime frame corresponds to, e.g., the time it takes for the vehicle 100to negotiate a curve. The vehicle maneuvers, planned and executed by theTSM or by the driver, can be associated with acceleration profiles andcurvature profiles which describe a desired vehicle velocity and turningfor a given maneuver. The TSM continuously requests the desiredacceleration profiles a_(req) and curvature profiles c_(req) from theVMM function 360 which performs force allocation to meet the requestsfrom the TSM in a safe and robust manner. The VMM function 360continuously feeds back capability information to the TSM functiondetailing the current capability of the vehicle in terms of, e.g.,forces, maximum velocities, and accelerations which can be generated.

FIG. 5 illustrates an example of a vehicle control architecturecomprising redundancy. The VMM function 360 in this architectureoperates with a time horizon of about 1 second or so, and continuouslytransforms the acceleration profiles a_(req) and curvature profilesc_(req) into control commands for controlling vehicle motion functions,actuated by the different MSDs 320, 340 of the vehicle 100 which reportback capabilities to the VMM, which in turn are used as constraints inthe vehicle control. The VMM function 360 performs vehicle state ormotion estimation 510, i.e., the VMM function 360 continuouslydetermines a vehicle state s comprising positions, speeds, accelerationsand articulation angles of the different units in the vehiclecombination by monitoring operations using various sensors 380 arrangedon the vehicle 100, often but not always in connection to the MSDs 320,340.

The result of the motion estimation 510, i.e., the estimated vehiclestate s, is input to a force generation module 520 which determines therequired global forces V=[V₁, V₂] for the different vehicle units tocause the vehicle 100 to move according to the requested accelerationand curvature profiles a_(req), c_(req). The required global forcevector V is input to an MSD coordination function 530 which allocateswheel forces and coordinates other MSDs such as steering and suspension,and generates MSD control signals 535 which are then sent to thedifferent MSD controllers 330. The coordinated MSDs then togetherprovide the desired lateral Fy and longitudinal Fx forces on the vehicleunits, as well as the required moments Mz, to obtain the desired motionby the vehicle combination 100.

FIG. 5 illustrates two redundant vehicle control systems, which can beused in combination or independently.

The first redundant system is a system for load estimation 540. Thissystem comprises a load estimation module 540 configured to use one ormore tire sensors 210 to determine vehicle load, which may compriseestimating tire normal forces F_(z) at one or more wheels. The VMM 360may then draw upon this input in case the primary load estimation systemof the vehicle 100 malfunctions. Systems for load estimation using,e.g., signals obtained from suspension systems and from other sourcesare known and will therefore not be discussed in more detail herein.

An example of how a tire sensor 210 can be used to predict the load on agiven wheel, i.e., a value associated with the normal force Fz acting onthe tire, will now be discussed. With reference to FIG. 9 , assuming anaccelerometer sensor has been mounted in, on, or in connection to a tire310. An example tire revolution reading from the accelerometer (lateral,longitudinal, and vertical coordinates) is schematically exemplified inFIG. 9 . The points A and B are the leading edge and the trailing edgein the contact patch of the tire. If some other tire of sensor is used,similar waveforms would be generated depending on if its acceleration orvelocity or position is measured, since they are related to each other.For example, using an optical sensor to measure tire deformation wouldprovide the displacement in a similar fashion, and the second orderderivative of the measured displacement would provide the accelerationof the sensor device.

The algorithms for determining load on a tire can be categorized intothree broad types of methods,

-   -   Data driven machine learning (ML) based approaches    -   Physics based analytical approaches    -   Physics guided machine learning approaches

In the Data driven ML based approach, a machine learning structure istrained using, e.g., tire Pressure, tire load, and road surfaceproperties. The truth labelling or training data used during trainingpresents example sensor output signals for a range of different tireloads, and the ML structure then adapts to be able to predict tire loadbased on the tire sensor output signal. The ML training may be performedoff-line and/or on-line, based on output from a primary load estimationsystem, such as a suspension-based system. The above features are chosensince they are the most sensitive parameters for the generated wave formout from the sensor. This ML model for estimating the load would bedeveloped through a large amount of data at different operatingconditions (different loads, velocity, tire pressure, road surface). Anexample of the curve's dependency on speed and load is schematicallyillustrated in FIG. 10A and FIG. 10B. As noticed, as the load increasesthe leading and trailing edge move away from each other with higheramplitude in the signal. Also, as velocity increases the amplitudeincreases but the leading and trailing edge tends to come closer to eachother. The artificial intelligence (AI) structure or the ML algorithmcan determine the normal load in this manner using conventional trainingtechniques.

In the case of physics based analytical approaches, with the help ofroad inclination angle and suspension movements, it is possible toquantify the road profile. Using this data, an inverse tire model asdiscussed above can be generated using a complete physics-based approachto determine what would be the load under the tire for that level ofacceleration and/or velocity and or displacement.

The physics guided ML approaches can be implemented as a mixture of thedata driven ML based approach and the physics based analytical approach.Assuming there is an accurate inverse tire model available, it ispossible to use that value in the ML training function or use it with areinforcement learning model for a neural network to train on the flyusing the physics-based approach in the error function for training thealgorithm. If there is no accurate inverse tire model available, thenthe ML structure could have tuneable parameters like tuneable Pacejkaparameters and that could be tuned as we train the model containing thatequation in the ML error function.

Assume an example truck with an air suspension system, where the load isestimated axle wise and not at individual wheel end. The brake forcedistribution function in the electronic braking system of the truck usesthe axle load to determine the brake pressure distribution axle wise tomeet the brake forces required. Usually on an unladen vehicle where therear load is relatively small compared to front axle load, the pressureon the rear is reduced compared to the pressure on the front. This is toutilize the peak friction and avoid unnecessary/pre-mature rear wheellock up and getting into ABS cycles.

When the primary load information has malfunctioned, the tire sensorswould kick in and provide the load data thus maintaining a rudimentaryfunction despite the primary system malfunction.

Further, this could also work in parallel for a L4 vehicle to determineindividual wheel end loads and the primary system could do a comparisonto this load info with the traditional air suspension load info and useone of the two based on confidence level and boundary conditions.

Tire sensors can also be used to determine slip ratio of a given wheel.Assuming that an accelerometer is used. An example tire revolutionreading from the accelerometer was schematically shown in FIG. 9 .

The points A and B are the leading edge and the trailing edge in thecontact patch. The reading within A and B define the contact patch data.If another sensor is used, similar waveforms would be generateddepending on if its acceleration/velocity/position since they arerelated to each other (derivatives). For example, using an opticalsensor would provide the displacement in a similar fashion, the secondorder derivative would provide the acceleration.

Direct ML black box training can be used to train the ML structure. Inthis methodology we use the during contact patch signal as shown in FIG.11 . FIG. 11 also shows the difference in the contact patch signal fordifferent slip ratios. This signal along with lateral and verticalsignal with the necessary features like load, surface, pressure, andvelocity (since they affect the signal characteristics primarily, theyneed to be input features) would be provided to the ML structure. Thehigh frequency in the figure in the during contact patch zone isobserved only because of the slippage and the contact patch length wouldalso change. Further, converting this signal to time domain using, e.g.,a fast Fourier transform (FFT).

In another method, illustrated in FIG. 12 , we would use the smart tireleading edge and trailing edge of one revolution to the next revolutionto estimate the time taken. Knowing the circumference of the tire, thiswould provide accurate wheel speed information. This wheel speedinformation would be used in the generic formula replacing Rω_(x),

$\lambda = \frac{{R\omega_{x}} - v_{x}}{\max\left( {{❘{R\omega}❘},{❘v_{x}❘}} \right)}$

Similarly, the rolling radius R can be estimated through pure ML orthrough physics based approach using the leading and trailing edgepoints and. This info can be used in the same generic info replacingonly R this time and using co from the wheel speed sensor.

Assuming we have a truck or other heavy-duty vehicle with an electronicbraking system based on pneumatics. The primary system could control anelectronic pressure modulator and the secondary system could controlanother electronic pressure modulator, both of these could lead to aSelect high or Select Low double check valve before being connected tothe wheel end. During a traction control event or antilock brakingevents or electronic stability program events, the primary system andsecondary system could function together with the Select high or SelectLow valve being in series allowing the correct pressure to flow to thewheel end. There could be fall back modes defined, only for certainfaults, the secondary system would take control and provide necessarypressures out of the electronic pressure modulators that it is connectedto.

In our case, during a traction control event, if there is a

a. Wheel speed sensor failure or

b. Primary control system ECU failure

c. Primary control system ECU Memory malfunction (it has corruptedvalues for Tire radius or tone ring pole count)

d. Absurd/out of range wheel slip values from primary ECU

e. Drastic difference between left and right only for one axle

There could be many more scenarios identified, our secondary controlunit would kick in and take control for accurate slip-based tractioncontrol for the L4 vehicles. This would be a safety concern for ASR, ESPand ABS for L4 vehicles to have redundancy. If there is no redundancy,then the vehicle will have to be stopped since there are risks of jackknifing and losing stability. Even during stop and go, if the rearwheels slip, there is high chance of oversteer and thus the vehicleshall not operate with such failures.

To summarize, there is disclosed herein a control system 500 forcontrolling one or more torque generating devices 320, 340 on aheavy-duty vehicle 100, the system 300, 500 comprising a primary sensorsystem 380 with a primary sensor control unit 220, 385 configured tointerpret an output signal 385 of the primary sensor system 380, whereinthe primary sensor control unit 220, 385 is configured to determine afirst load value associated with the heavy-duty vehicle 100, and one ormore tire sensor devices 210 mounted on one or more tires 120, 140, 160,180 of the heavy-duty vehicle 100, and a tire sensor control unit 350configured to interpret an output signal 355 of the one or more tiresensor devices 210, wherein the tire sensor control unit 220, 385 isconfigured to determine a second load value associated with theheavy-duty vehicle 100, wherein the control system 500 is arranged tobase control of the heavy-duty vehicle 100 on the second load value incase of malfunction in the primary sensor system 380 and/or in theprimary sensor control unit 220, 385, and on the first load valueotherwise.

The tire sensor system is independent from the primary sensor system,and therefore advantageously used for redundancy purposes. The outputsignals from the tire sensors can be used at least temporarily, e.g., inorder to bring the heavy-duty vehicle to a full stop in case ofmalfunction in the primary sensor system. It is also an advantage thatthe tire sensors are mounted in, on, or in connection to the tires onthe wheel, and not taking up valuable space on the axles of the vehicleor the scarce chassis space.

According to aspects, the primary sensor system 380 comprises a sensorconfigured in connection to a suspension system of the heavy-dutyvehicle 100.

According to aspects, the one or more tire sensor devices 210 comprisesany of; an accelerometer, a strain gauge, and an optical sensor, whereinthe output data of the tire sensor control unit 350 comprises the secondload value.

According to aspects, the tire sensor control unit 350 is configured tointerpret the output signals 355 of the one or more tire sensor devices210 based on a machine learning algorithm and or a physics guidedmachine learning algorithm, as discussed above.

According to aspects, the one or more tire sensor devices 210 comprisesany of; an accelerometer, a strain gauge, and an optical sensor, whereinthe output data of the tire sensor control unit 350 comprises one ormore wheel speeds.

According to aspects, the one or more tire sensor devices 210 comprisesa satellite positioning system receiver, wherein the output data of thetire sensor control unit 350 comprises a vehicle speed over ground.

According to aspects, the output data of the tire sensor control unit350 comprises a wheel slip ratio associated with a wheel on theheavy-duty vehicle 100.

The second redundant system comprises a module 550 for traction controland/or anti-lock braking. This module also bases its operation on inputdata obtained from one or more tire sensors 210. If some crucialcomponent, either a hardware component or a software componentmalfunctions, the secondary traction control system and/or the secondaryABS can step in and provide at least a rudimentary form of support, atleast until the vehicle 100 can be brought to a full stop is a safemanner.

Consequently, there is also disclosed herein a motion control system300, 500 for controlling one or more torque generating devices 320, 340on a heavy-duty vehicle 100. The system 300, 500 comprises a primarysensor system 380 with a primary sensor control unit 220, 385 configuredto interpret an output signal 385 of the primary sensor system 380, oneor more tire sensor devices 210 mounted on one or more tires 120, 140,160, 180 of the heavy-duty vehicle 100, and a tire sensor control unit350 configured to interpret an output signal 355 of the one or more tiresensor devices 210. The motion control system 300, 500 is arranged tobase motion control of the heavy-duty vehicle 100 on output data of thetire sensor control unit 350 in case of malfunction in the primarysensor system 380 and/or in the primary sensor control unit 220, 385,and on output data of the primary sensor control unit 220, 385otherwise. Again, the tire sensor system on the heavy-duty vehicle isused to provide an independent and separate sensor system which canfunction as a back-up in case the primary sensor system on theheavy-duty vehicle fails for some reason.

According to aspects, the primary sensor system 380 comprises one ormore wheel speed sensors, where the primary sensor control unit 220, 385is configured to perform an anti-lock braking system, ABS, function.

According to aspects, the primary sensor system 380 comprises one ormore wheel speed sensors, where the primary sensor control unit 220, 385is configured to perform a traction control, ASR, function.

According to aspects, the one or more tire sensor devices 210 comprisesany of; an accelerometer, a strain gauge, and an optical sensor, whereinthe output data of the tire sensor control unit 350 comprises one ormore wheel speeds.

According to aspects, the one or more tire sensor devices 210 comprisesa satellite positioning system receiver, wherein the output data of thetire sensor control unit 350 comprises a vehicle speed over ground.

According to aspects, the output data of the tire sensor control unit350 comprises a wheel slip ratio associated with a wheel on theheavy-duty vehicle 100.

According to aspects, the primary sensor control unit 220, 385 isconfigured to control a primary valve system for brake control of theheavy-duty vehicle 100, wherein the tire sensor control unit 350 isconfigured to control a secondary valve system for brake control of theheavy-duty vehicle 100 separate from the primary valve system.

According to aspects, the tire sensor control unit 350 is configured tointerpret the output signals 355 of the one or more tire sensor devices210 based on a machine learning algorithm.

According to aspects, the tire sensor control unit 350 is configured tointerpret the output signals 355 of the one or more tire sensor devices210 based on a physics guided machine learning algorithm.

By determining vehicle unit motion using, e.g., global positioningsystems, vision-based sensors, wheel speed sensors, radar sensors and/orlidar sensors, and translating this vehicle unit motion into a localcoordinate system of a given wheel 310 (in terms of, e.g., longitudinaland lateral velocity components), it becomes possible to accuratelyestimate wheel slip in real time by comparing the vehicle unit motion inthe wheel reference coordinate system to data obtained from the wheelspeed sensor arranged in connection to the wheel 310. Alternatively, aredundant system based on smart tires with tire sensors and a tiresensor control unit can be used to determine wheel slip accurately inreal time.

A tire model, which will be discussed in more detail in connection toFIG. 4 below, can be used to translate between a desired longitudinaltire force Fx, for a given wheel i and an equivalent wheel slip □_(i)for the wheel. Wheel slip □ relates to a difference between wheelrotational velocity and speed over ground and will be discussed in moredetail below. Wheel speed □ is a rotational speed of the wheel, given inunits of, e.g., rotations per minute (rpm) or angular velocity in termsradians/second (rad/sec) or degrees/second (deg/sec).

Herein, a tire model is a model of wheel behavior which describes wheelforce generated in longitudinal direction (in the rolling direction)and/or lateral direction (orthogonal to the longitudinal direction) asfunction of wheel slip. In “Tire and vehicle dynamics”, Elsevier Ltd.2012, ISBN 978-0-08-097016-5, Hans Pacejka covers the fundamentals oftire models. See, e.g., chapter 7 where the relationship between wheelslip and longitudinal force is discussed.

To summarize, in an example implementation of a vehicle control system500, the VMM function 360 manages both force generation and MSDcoordination, i.e., it determines what forces that are required at thevehicle units in order to fulfil the requests from the TSM function 370,or from a driver of the vehicle 100, for instance to accelerate thevehicle according to a requested acceleration profile requested by TSMand/or to generate a certain curvature motion by the vehicle alsorequested by TSM. The forces may comprise e.g., yaw moments Mz,longitudinal forces Fx and lateral forces Fy, as well as different typesof torques to be applied at different wheels.

The interface 365 between VMM and MSDs capable of delivering torque tothe vehicle's wheels has, traditionally, been focused on torque basedrequests to each MSD from the VMM without any consideration towardswheel slip. However, this approach has significant performancelimitations. In case a safety critical or excessive slip situationarises, then a relevant safety function (traction control, anti-lockbrakes, etc.) operated on a separate control unit normally steps in andrequests a torque override in order to bring the slip back into control.The problem with this approach is that since the primary control of theactuator and the slip control of the actuator are allocated to differentelectronic control units (ECUs), the latencies involved in thecommunication between them significantly limits the slip controlperformance. Moreover, the related actuator and slip assumptions made inthe two ECUs that are used to achieve the actual slip control can beinconsistent and this in turn can lead to sub-optimal performance.

Significant benefits can be achieved by instead using a wheel speed orwheel slip based request on the interface 365 between VMM and the MSDcontroller or controllers 330, thereby shifting the difficult actuatorspeed control loop to the MSD controllers, which generally operate witha much shorter sample time compared to that of the VMM function. Such anarchitecture can provide much better disturbance rejection compared to atorque based control interface and thus improves the predictability ofthe forces generated at the tire road contact patch.

Longitudinal wheel slip λ may, in accordance with SAE J670 (SAE VehicleDynamics Standards Committee Jan. 24, 2008) be defined as

$\lambda = \frac{{R\omega_{x}} - v_{x}}{\max\left( {{❘{R\omega}❘},{❘v_{x}❘}} \right)}$

where R is an effective wheel radius in meters, ω_(x) is the angularvelocity of the wheel, and v_(x) is the longitudinal speed of the wheel(in the coordinate system of the wheel). Thus, λ is bounded between −1and 1 and quantifies how much the wheel is slipping with respect to theroad surface. Wheel slip is, in essence, a speed difference measuredbetween the wheel and the vehicle. Thus, the herein disclosed techniquescan be adapted for use with any type of wheel slip definition. It isalso appreciated that a wheel slip value is equivalent to a wheel speedvalue given a velocity of the wheel over the surface, in the coordinatesystem of the wheel.

Since the wheel slip may be crucial for vehicle motion management, itbecomes important to ensure that a reliable estimate of wheel slip isalways available. The tire sensor based systems increase thisreliability. The same is true for normal load, which plays an importantpart in, e.g., determining peak available wheel force, as will bediscussed in connection to FIG. 4 below. The vehicle motion managementsystems discussed herein comprise redundant systems for estimatingnormal load, and can therefore function even if the primary loadestimation system fails for some reason.

The VMM 360 and optionally also the MSD control unit 330 maintainsinformation on v_(x) (in the reference frame of the wheel), while awheel speed sensor 380 or the like can be used to determine ω_(x) (therotational velocity of the wheel).

In order for a wheel (or tire) to produce a wheel force, slip mustoccur. For smaller slip values the relationship between slip andgenerated force are approximately linear, where the proportionalityconstant is often denoted as the slip stiffness of the tire. A tire 310is subject to a longitudinal force F_(x), a lateral force F_(y), and anormal force F_(z). The normal force F_(z) is key to determining someimportant vehicle properties. For instance, the normal force to a largeextent determines the achievable lateral tire force F_(y) by the wheelsince, normally, F_(y)≤μF_(z), where μ is a friction coefficientassociated with a road friction condition. The maximum available lateralforce for a given lateral slip can be described by the so-called MagicFormula as described in “Tire and vehicle dynamics”, Elsevier Ltd. 2012,ISBN 978-0-08-097016-5, by Hans Pacejka.

FIG. 4 is a graph showing an example of achievable tire force asfunction of wheel slip. The longitudinal tire force Fx shows an almostlinearly increasing part 410 for small wheel slips, followed by a part420 with more non-linear behaviour for larger wheel slips. Theobtainable lateral tire force Fy decreases rapidly even at relativelysmall longitudinal wheel slips. It is desirable to maintain vehicleoperation in the linear region 410, where the obtainable longitudinalforce in response to an applied brake command is easier to predict, andwhere enough lateral tire force can be generated if needed. To ensureoperation in this region, a wheel slip limit □_(LIM) on the order of,e.g., 0.1, can be imposed on a given wheel. For larger wheel slips,e.g., exceeding 0.1, a more non-linear region 420 is seen. Control of avehicle in this region may be difficult and is therefore often avoided.It may be interesting for traction in off-road conditions and the likewhere a larger slip limit for traction control might be preferred, butnot for on-road operation.

This type of tire model can be used by the VMM 360 to generate a desiredtire force at some wheel. Instead of requesting a torque correspondingto the desired tire force, the VMM can translate the desired tire forceinto an equivalent wheel slip (or, equivalently, a wheel speed relativeto a speed over ground) and request this slip instead. The mainadvantage being that the MSD control device 330 will be able to deliverthe requested torque with much higher bandwidth by maintaining operationat the desired wheel slip, using the vehicle speed v_(x) and the wheelrotational velocity ω_(x).

The VECU 110, 115 can be arranged to store a pre-determined inverse tiremodel f⁻¹ in memory, e.g., as a look-up table. The inverse tire model isarranged to be stored in the memory as a function of the currentoperating condition of the wheel 310. This means that the behavior ofthe inverse tire model is adjusted in dependence of the operatingcondition of the vehicle, which means that a more accurate model isobtained compared to one which does not account for operating condition.The model which is stored in memory can be determined based onexperiments and trials, or based on analytical derivation, or acombination of the two. For instance, the control unit can be configuredto access a set of different models which are selected depending on thecurrent operating conditions. One inverse tire model can be tailored forhigh load driving, where normal forces are large, another inverse tiremodel can be tailored for slippery road conditions where road frictionis low, and so on. The selection of a model to use can be based on apre-determined set of selection rules. The model stored in memory canalso, at least partly, be a function of operating condition. Thus, themodel may be configured to take, e.g., normal force or road friction asinput parameters, thereby obtaining the inverse tire model in dependenceof a current operating condition of the wheel 310. It is appreciatedthat many aspects of the operating conditions can be approximated bydefault operating condition parameters, while other aspects of theoperating conditions can be roughly classified into a smaller number ofclasses. Thus, obtaining the inverse tire model in dependence of acurrent operating condition of the wheel 310 does not necessarily meanthat a large number of different models need to be stored, or acomplicated analytical function which is able to account for variationin operating condition with fine granularity. Rather, it may be enoughwith two or three different models which are selected depending onoperating condition. For instance, one model to be used when the vehicleis heavily loaded and another model to be used otherwise. In all cases,the mapping between tire force and wheel slip changes in some way independence of the operating condition, which improves the precision ofthe mapping.

The inverse tire model may also be implemented at least partly as anadaptive model configured to automatically or at leastsemi-automatically adapt to the current operating conditions of thevehicle. This can be achieved by constantly monitoring the response of agiven wheel in terms of wheel force generated in response to a givenwheel slip request, and/or monitoring the response of the vehicle 100 inresponse to the wheel slip requests. The adaptive model can then beadjusted to more accurately model the wheel forces obtained in responseto a given wheel slip request from a wheel.

FIG. 6A is a flow chart illustrating a method which summarizes some ofthe techniques discussed above. There is illustrated a computerimplemented method performed by a motion control system 300, 500 forcontrolling one or more torque generating devices 320, 340 on aheavy-duty vehicle 100. The method comprising:

configuring Sa1 a primary sensor system 380 with a primary sensorcontrol unit 220, 385, interpreting Sa2 an output signal 385 of theprimary sensor system 380 by the primary sensor control unit 220, 385,

configuring Sa3 one or more tire sensor devices 210 on one or more tires120, 140, 160, 180 of the heavy-duty vehicle, 100 and a tire sensorcontrol unit 350,

interpreting Sa4 an output signal 355 of the one or more tire sensordevices 210 by the tire sensor control unit 350, and

performing Sa5 motion control of the heavy-duty vehicle 100 based onoutput data of the tire sensor control unit 350 in case of malfunctionin the primary sensor system 380 and/or in the primary sensor controlunit 220, 385, and on output data of the primary sensor control unit220, 385 otherwise.

FIG. 6B is also a flow chart illustrating a method which summarizes someother parts of the techniques discussed above. There is illustrated acomputer implemented method performed by a motion control system 300,500 for controlling one or more torque generating devices 320, 340 on aheavy-duty vehicle 100. The method comprises

configuring Sa1 a primary sensor system 380 with a primary sensorcontrol unit 220, 385, interpreting Sa2 an output signal 385 of theprimary sensor system 380 by the primary sensor control unit 220, 385,

configuring Sa3 one or more tire sensor devices 210 on one or more tires120, 140, 160, 180 of the heavy-duty vehicle 100 and a tire sensorcontrol unit 350,

interpreting Sa4 an output signal 355 of the one or more tire sensordevices 210 by the tire sensor control unit 350, and

performing Sa5 motion control of the heavy-duty vehicle 100 based onoutput data of the tire sensor control unit 350 in case of malfunctionin the primary sensor system 380 and/or in the primary sensor controlunit 220, 385, and on output data of the primary sensor control unit220, 385 otherwise.

FIG. 7 schematically illustrates, in terms of a number of functionalunits, the components of a control unit 110, 115 according toembodiments of the discussions herein. This control unit 110, 115 may becomprised in the vehicle 100, e.g., in the form of a VMM unit.Processing circuitry 710 is provided using any combination of one ormore of a suitable central processing unit CPU, multiprocessor,microcontroller, digital signal processor DSP, etc., capable ofexecuting software instructions stored in a computer program product,e.g., in the form of a storage medium 730. The processing circuitry 710may further be provided as at least one application specific integratedcircuit ASIC, or field programmable gate array FPGA.

Particularly, the processing circuitry 710 is configured to cause thecontrol unit 110, 115 to perform a set of operations, or steps, such asthe methods discussed in connection to FIGS. 6A and 6B. For example, thestorage medium 730 may store the set of operations, and the processingcircuitry 710 may be configured to retrieve the set of operations fromthe storage medium 730 to cause the control unit 110, 115 to perform theset of operations. The set of operations may be provided as a set ofexecutable instructions. Thus, the processing circuitry 710 is therebyarranged to execute methods as herein disclosed.

The storage medium 730 may also comprise persistent storage, which, forexample, can be any single one or combination of magnetic memory,optical memory, solid state memory or even remotely mounted memory.

The control unit 110, 115 may further comprise an interface 720 forcommunications with at least one external device such as a tire sensor.As such the interface 720 may comprise one or more transmitters andreceivers, comprising analogue and digital components and a suitablenumber of ports for wireline or wireless communication.

The processing circuitry 710 controls the general operation of thecontrol unit 110, 115, e.g., by sending data and control signals to theinterface 720 and the storage medium 730, by receiving data and reportsfrom the interface 720, and by retrieving data and instructions from thestorage medium 730. Other components, as well as the relatedfunctionality, of the control node are omitted in order not to obscurethe concepts presented herein.

FIG. 8 illustrates a computer readable medium 810 carrying a computerprogram comprising program code means 820 for performing the methodsillustrated in FIG. 6 and the techniques discussed herein, when saidprogram product is run on a computer. The computer readable medium andthe code means may together form a computer program product 800.

1. A control system for controlling one or more torque generatingdevices on a heavy-duty vehicle, the system comprising: a primary sensorsystem with a primary sensor control unit configured to interpret anoutput signal of the primary sensor system, wherein the primary sensorcontrol unit is configured to determine a first load value associatedwith the heavy-duty vehicle, and one or more tire sensor devices mountedon one or more tires of the heavy-duty vehicle, and a tire sensorcontrol unit configured to interpret an output signal of the one or moretire sensor devices, wherein the tire sensor control unit is configuredto determine a second load value associated with the heavy-duty vehicle,wherein the control system is arranged to base control of the heavy-dutyvehicle on the second load value in case of malfunction in the primarysensor system and/or in the primary sensor control unit, and on thefirst load value otherwise.
 2. The control system of claim 1, whereinthe primary sensor system comprises a sensor configured in connection toa suspension system of the heavy-duty vehicle.
 3. The control system ofclaim 1, wherein the one or more tire sensor devices comprises any of:an accelerometer, a strain gauge, and an optical sensor, wherein theoutput data of the tire sensor control unit comprises the second loadvalue.
 4. The control system of claim 1, wherein the tire sensor controlunit is configured to interpret the output signals of the one or moretire sensor devices based on a machine learning algorithm.
 5. Thecontrol system of claim 1, wherein the tire sensor control unit isconfigured to interpret the output signals of the one or more tiresensor devices based on a physics guided machine learning algorithm. 6.The control system of claim 1, wherein the one or more tire sensordevices comprises any of: an accelerometer, a strain gauge, and anoptical sensor, wherein the output data of the tire sensor control unitcomprises one or more wheel speeds.
 7. The control system of claim 1,wherein the one or more tire sensor devices comprises a satellitepositioning system receiver, wherein the output data of the tire sensorcontrol unit comprises a vehicle speed over ground.
 8. The controlsystem of claim 7, wherein the output data of the tire sensor controlunit comprises a wheel slip ratio associated with a wheel on theheavy-duty vehicle.
 9. A computer implemented method performed in acontrol system for controlling one or more torque generating devices ona heavy-duty vehicle, the method comprising: configuring a primarysensor system with a primary sensor control unit to determine a firstload value associated with the heavy-duty vehicle, configuring one ormore tire sensor devices on one or more tires of the heavy-duty vehicle,and a tire sensor control unit to interpret an output signal of the oneor more tire sensor devices, determining a second load value associatedwith the heavy-duty vehicle by the tire sensor control unit, andperforming motion control of the heavy-duty vehicle based on the secondload value in case of malfunction in the primary sensor system and/or inthe primary sensor control unit, and on the first load value otherwise.10. A computer program comprising program code means for performing thesteps of claim 9 when the program is run on a computer or on processingcircuitry of a control unit.
 11. A computer readable medium carrying acomputer program comprising program code means for performing the stepsof claim 9 when the program product is run on a computer or onprocessing circuitry of a control unit.